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The Sierpinski gasket fractal antenna is most popular structure in the domain of fractal antennas. This fractal antenna has multi-band performance, and hence, the design of this antenna for the desired frequencies is a challenging problem. The artificial intelligence tools like artificial neural networks, fuzzy logic systems, bio-inspired optimization techniques are appropriate to provide accurate design solution in such cases. In this paper, three most popular bio-inspired optimization algorithms: genetic algorithms, particle swarm optimization (PSO), and bacterial foraging optimization, have been proposed to solve the design issues of Sierpinski gasket pre-fractal antenna. Their performances are analyzed and are compared with the experimental results. A simplified expression for calculation of resonant frequency of Sierpinski gasket pre-fractal antenna is proposed and is used as the objective function. Finally, the effectiveness is compared on the basis of three different measures: mean absolute percentage error, the average time taken by the models to evaluate the results, and the coefficient of correlation. The results indicate that the PSO algorithm is most suitable for this type of antenna.

Artificial neural networks due to their general-purpose nature are used to solve problems in diverse fields. Artificial neural networks (ANNs) are very useful for fractal antenna analysis as the development of mathematical models of such antennas is very difficult due to complex shapes and geometries. As such empirical approach doing experiments is costly and time consuming, in this paper, application of artificial neural networks analysis is presented taking the Sierpinski gasket fractal antenna as an example. The performance of three different types of networks is evaluated and the best network for this type of applications has been proposed. The comparison of ANN results with experimental results validates that this technique is an alternative to experimental analysis. This low cost method of antenna analysis will be very useful to understand various aspects of fractal antennas.